Remote Sensing Applications-Society and Environment最新文献

{"title":"LMFAN: Lightweight multi-scale feature aggregation network with channel pruning and knowledge distillation for ship detection in remote sensing images","authors":"Kequan Shi ,&nbsp;Qi Li ,&nbsp;Hengchao Li ,&nbsp;Pan Xu ,&nbsp;Peng Zhang ,&nbsp;Sen Yang ,&nbsp;Hongna Zhu","doi":"10.1016/j.rsase.2025.101692","DOIUrl":"10.1016/j.rsase.2025.101692","url":null,"abstract":"<div><div>Ship detection in remote sensing images, especially in synthetic aperture radar (SAR) images holds significant application value in maritime traffic monitoring, military reconnaissance, and related domains. To address the accuracy degradation caused by complex maritime background interference, multi-scale target coexistence, and weak feature representation of small targets in SAR images, we propose a lightweight multi-scale feature aggregation network (LMFAN) based on YOLO11. Firstly, a gram polynomial-driven dynamic convolution module that employs differentiable Gram-Kolmogorov basis functions is designed to expand the receptive field of convolutional kernels, enhancing coarse-grained feature representation. Secondly, we employ an enhanced spatial-orientation pyramid utilizing an Omni-Kernel to fuse global, large-local, and local detailed features, significantly improving feature responses for small targets. Thirdly, we design a multi-scale detail-sharing decoder incorporating detail-enhanced and shared convolution to preserve contextual information while reducing computational overhead. Moreover, a dynamic channel pruning strategy with channel-wise cascaded optimization and a channel-wise knowledge distillation with the Kullback–Leibler divergence loss function is introduced to resolves the feature coupling issue in dense target scenarios. Experimental results demonstrate that compared to baseline YOLO11n model, LMFAN achieves mAP<span><math><mi>@</mi></math></span>0.5:0.95 improvements of 3.6%, 4.9%, and 1.5% on the SSDD, HRSID, and SAR-Ship-Dataset, respectively. Additional validation confirms the model’s superior generalization capability. Compared with state-of-the-art methods, the LMFAN can achieve better accuracy and fewer parameters.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101692"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brittle tectonic feature in the external part of the Variscan belt (Eastern Anti-Atlas, Morocco): A combined field and remote sensing approach","authors":"Mohammad Taadid ,&nbsp;Ahmed Attou ,&nbsp;Ayoub Aabi ,&nbsp;Younes Hejja ,&nbsp;Abdellah Nait-Bba ,&nbsp;Lahssen Baidder ,&nbsp;Lahsen Achkouch ,&nbsp;Younesse El Cheikh ,&nbsp;Said Ou Moua ,&nbsp;Ibrahim Bouazama ,&nbsp;Rachid Ahmed","doi":"10.1016/j.rsase.2025.101731","DOIUrl":"10.1016/j.rsase.2025.101731","url":null,"abstract":"<div><div>Located in the external part of the Variscan belt, the Ougnat massif in the eastern Anti-Atlas of Morocco represents one of the repeatedly deformed inliers from the Precambrian onward. This study presents a detailed structural analysis covering the outcrops of Precambrian basement and Paleozoic cover in the Ougnat massif. The interpretation is carried through remote sensing-based lineament mapping in this arid area. The methodology entails processing a Landsat-8 OLI satellite image using a Directional Filter to accentuate the various structural lineaments across different scales. A Digital Elevation Model (DEM) is used to map all major structural lineaments.</div><div>The analysis led to the identification of 4719 lineaments extracted manually, as well as 2330 lineaments extracted automatically from the first vector of the principal component (PCA1). Moreover, 213 major lineaments, mainly related to large morphological structures, were identified from the digital elevation model (DEM). All lineaments are organized along four main directions, NE-SW to ENE-WSW, ESE-WNW to SE-NW, and NNW-SSE to N-S. Mapped fault networks are more common in the Precambrian basement than in the Paleozoic cover, indicating the higher mechanical strength and rigid rheology of the Pan-African basement rocks. Many previously identified or suspected faults have been confirmed, others have been extended, and new major faults have been revealed. These structures reflect a long tectonic history, with multiple reactivation phases from the Precambrian to the present day. The results provide new insights into the structural evolution of the Ougnat massif and its broader tectonic framework within the Variscan belt.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101731"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regionalization of oceanic waters based on satellite bio-optical properties in the central and southern Gulf of Mexico","authors":"Uriel Mirabal ,&nbsp;Lorena Linacre ,&nbsp;Reginaldo Durazo ,&nbsp;Eduardo Santamaría-del-Ángel ,&nbsp;Enric Pallàs-Sanz ,&nbsp;José R. Lara-Lara","doi":"10.1016/j.rsase.2025.101695","DOIUrl":"10.1016/j.rsase.2025.101695","url":null,"abstract":"<div><div>The absorption or scattering of photosynthetically active radiation (PAR) in the ocean by its components is crucial in determining the extent of the euphotic zone. This stratum is the ocean's uppermost layer, where light availability governs primary productivity in marine ecosystems. This study aims to bio-optically categorize Gulf of Mexico oceanic waters based on regional differences in water transparency and the euphotic zone depth using remotely sensed diffuse attenuation coefficient data (Kd490) validated with <em>in situ</em> observations (KdPAR) from cruises conducted between May 2016 and June 2019. An overestimation of about 15 % of remotely sensed depths was found, which was significantly reduced using in-water records. A 22-year climatological analysis on the calibrated euphotic zone depths derived from satellite-based Kd identified three optical regions in the Gulf of Mexico (<em>I</em>, <em>IA</em>, and <em>IB</em>). Deeper euphotic zones were observed in Region <em>I</em> (in central waters), while Region <em>IB</em> (in the Bay of Campeche) had shallower euphotic zones. The differences in PAR light penetration were linked to variations in chlorophyll-<em>a</em>, which were modulated by mesoscale circulation and river discharges. Our findings support the extent of the euphotic zone down to 0.1 % of incident surface light, especially in Region <em>I</em>, where the large anticyclonic eddies detached from the Loop Current have the most influence in the oceanic waters of the Gulf of Mexico. This study contributes to understanding important optical variables that can be used to improve models for estimating primary productivity and carbon fluxes in oligotrophic surface layers.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101695"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial patterns of carbon storage in relation to protection status and road infrastructure in an insular forest landscape","authors":"Gabriel E. Suárez-Fernández ,&nbsp;Savvas Zotos ,&nbsp;Joaquín Martínez-Sánchez ,&nbsp;Marilena Stamatiou ,&nbsp;Elli Tzirkalli ,&nbsp;Ioannis N. Vogiatzakis ,&nbsp;Pedro Arias","doi":"10.1016/j.rsase.2025.101713","DOIUrl":"10.1016/j.rsase.2025.101713","url":null,"abstract":"<div><div>Forests play a crucial role in climate change mitigation through carbon storage. Nevertheless, these ecosystems face increasing threats from human activities, such as infrastructure development and Land Use/Land Cover (LULC) changes. To date, limited research has focused on understanding how roads impact carbon stocks in forests, and how this relation is influenced by protection regimes, especially on islands. This study on the island of Cyprus aims to assess Machine Learning (ML) techniques for estimating key forest variables such as Canopy Cover (CC) and to analyze the spatial dynamics of carbon stocks around roads in relation to LULCs and protection regimes. Remote Sensing (RS) data, including Landsat imagery and orthophotos, are combined with ML to create an ensemble model for detailed LULC classifications. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) tool is utilized to estimate carbon stocks for each LULC and statistical analysis is used to evaluate interactions between forests, roads, and protection regimes. The analysis revealed that protected sites store significantly 17 % more carbon than unprotected areas whilst proximity to roads exhibits complex effects on carbon stocks, with varying patterns depending on the protection status. The ensemble model outperforms individual models, achieving 92 % accuracy and a kappa of 0.91, showing the advantages of combining algorithms for more robust predictions. The research highlights the impact of integrating ML with ecosystem service models to improve understanding of interactions between roads, LULC, and forests. It also emphasizes the importance of conservation and roadside vegetation management for ecosystem resilience and sustainable carbon storage.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101713"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive feature extraction and attention-based segmentation network for remote sensing imagery","authors":"Aneeqah Azmat ,&nbsp;Basim Azam ,&nbsp;Farrukh Aziz Bhatti ,&nbsp;Sheheryar Khan","doi":"10.1016/j.rsase.2025.101679","DOIUrl":"10.1016/j.rsase.2025.101679","url":null,"abstract":"<div><div>Semantic segmentation of land cover types is a pivotal task in remote sensing, essential for applications in urban planning, environmental monitoring, disaster management, and agriculture. Accurate segmentation is challenged by imbalanced class distributions and ambiguous boundaries. This paper introduces AdaptiveFusionNet, a novel architecture designed to address heterogeneous complexities in remote sensory image, by leveraging adaptive, multi-scale feature extraction and efficient fusion mechanisms. The architecture comprises three core modules: the Adaptive Pixel Encoder (APE), which enhances pixel-level feature extraction across multiple scales; the Fusion Atrous Pooling (FAP), which effectively integrates contextual information using atrous convolutions; and the Parallel Attention Decoder (PAD), which refines segmentation boundaries through attention-enhanced upsampling. Evaluated on the high-resolution Gaofen 2 dataset, AdaptiveFusionNet demonstrates substantial improvements in key performance metrics, achieving an overall Intersection over Union (IoU) of 71% and excelling in Precision, Recall, and F1 score across various land cover classes, including urban areas, vegetation, water bodies, and infrastructure. An ablation study is presented to validate AdaptiveFusionNet’s superiority over existing architectures. The results establish AdaptiveFusionNet as an improved architecture for high-resolution land cover segmentation in terms of both accuracy and computational efficiency.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101679"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of Drought Risk Index model using Ocean temperature change, vegetation health, and surface soil moisture in Thailand's agricultural areas","authors":"Soravis Supavetch ,&nbsp;Aphisit Phonchob ,&nbsp;Woranut Chansury ,&nbsp;Panu Nueangjumnong ,&nbsp;Sirilux Noikeaing ,&nbsp;Anuphao Aobpaet","doi":"10.1016/j.rsase.2025.101723","DOIUrl":"10.1016/j.rsase.2025.101723","url":null,"abstract":"<div><div>Thailand, located in Southeast Asia, is among the countries most severely affected by climate variability, particularly droughts driven by the El Niño–Southern Oscillation (ENSO). Effective drought monitoring is crucial for agricultural management and disaster mitigation. This research aims to enhance the Drought Risk Index (DRI) model by integrating key parameters: the Ocean Niño Index (ONI) representing ENSO phases, the Vegetation Health Index (VHI), and Surface Soil Moisture (SSM) derived from SMAP satellite data. The study employed ONI data to categorise climatic conditions into higher and lower Pacific Ocean temperature periods, coupled with weekly VHI from the Suomi-NPP satellite and monthly averaged SSM data to recalibrate and improve the drought sensitivity of the existing DRI model. Results indicate that incorporating ENSO-related parameters significantly enhances the ability of the DRI to detect agricultural drought conditions, particularly during severe drought events, as observed in 2019. However, the short data series (2019–2022) poses limitations in long-term drought trend analysis and potential overfitting of model parameters. Nevertheless, the improved DRI model provides greater accuracy for drought monitoring and can effectively support decision-making processes for drought resilience and recovery in Thailand's agricultural sector.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101723"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term monitoring of Water Hyacinth in Lake Nokoué, Benin, West Africa","authors":"Priscilla Baltezar ,&nbsp;Ufuoma Ovienmhada ,&nbsp;David Lagomasino ,&nbsp;Lola Fatoyinbo ,&nbsp;Seamus Lombardo ,&nbsp;Metogbe Belfrid Djihouessi ,&nbsp;Djigbo Félicien Badou ,&nbsp;Gildas Tomavo ,&nbsp;Fohla Mouftaou ,&nbsp;Danielle Wood","doi":"10.1016/j.rsase.2025.101699","DOIUrl":"10.1016/j.rsase.2025.101699","url":null,"abstract":"<div><div>Water hyacinth is a globally recognized invasive aquatic plant known for its significant environmental impact and the substantial costs associated with its management. Its proliferation has caused widespread damage across Lake Nokoué in southern Benin, home to fishing communities that practice traditional fishing techniques called <em>Acadja</em>. Although these fishing structures increase fishing yields, they also exacerbate the water hyacinth infestation rate. This study, therefore, models the extent of water hyacinth, <em>Acadja</em> with attached water hyacinth, other land, and other vegetation in the Lake Nokoué area using Landsat Collection 2 Tier 1 and Sentinel-1 C-band Synthetic Aperture Radar imagery from 2015 to 2022 with Random Forest machine learning. Seventeen predictors were selected to model each land cover, including five spectral indices, seven spectral bands, two radar bands, and three terrain predictors. The model mapped a total of 17,413.4 ha for water, 2907.5 ha for water hyacinth, 1780.6 ha for <em>Acadja</em>, 2128.6 ha for other land, and 8289 ha for other vegetation areas by the end of 2022. The rate of change in the region since 2015 was −6.8 % (water), +149.7 % (water hyacinth), +726.1 % (<em>Acadja</em>), −20.6 % (other land), and −15 % (other vegetation) for each class. A separate method was also tested to compare the supervised modeling to an unsupervised method. Otsu segmentation was used for the same study period. Otsu detected 614 ha of vegetation for 2015 and increased to 1133 ha by 2022, but results indicate this method is unreliable. Vegetation found in Lake Nokoué was also assessed monthly from 2000 to 2022 using manual segmentation of a harmonic Landsat time series. By 2022, results indicated that infestations consistently maxed out during December and January and exponentially expanded. Although infestations traditionally peaked during November and December, the study found that lake vegetation increased by 941 % and 304 % for the high- (September to December) and low-water (January to May) seasons.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101699"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of debris flow hazard based on multi-source remote sensing data: A case study of Dulongjiang Township, Yunnan Province","authors":"Zezhong Zheng ,&nbsp;Zixuan Teng ,&nbsp;Chuhang Xie ,&nbsp;Yi Ma ,&nbsp;Gang Wen ,&nbsp;Fangrong Zhou ,&nbsp;Huahui Tang","doi":"10.1016/j.rsase.2025.101719","DOIUrl":"10.1016/j.rsase.2025.101719","url":null,"abstract":"<div><div>Debris flows, characterized as a highly destructive natural hazard with sudden onset and extensive impact zones, pose severe threats to mountainous regions worldwide, particularly in geologically vulnerable areas like China's Yunnan Province. Accurate prediction of these events is crucial for disaster prevention and mitigation, yet it remains challenging due to the complex interplay of environmental factors. This investigation presents an advanced debris flow prediction framework for Dulongjiang Township in Yunnan Province by integrating multi-source remote sensing data and machine learning (ML) techniques. By combining optical, Synthetic Aperture Radar (SAR), and topographic datasets, we develop a high-resolution grid-based approach that captures both static predisposing factors and dynamic precursory signals. Among six evaluated ML models, the Multilayer Perceptron (MLP) demonstrated superior performance, achieving an Area Under the Precision-Recall Curve (AUPRC) score of 0.94, with recall and precision of 0.93 and 0.86, respectively, in debris flow prediction. The incorporation of Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR)-based surface deformation significantly enhanced prediction accuracy compared to traditional static-factor models, establishing a novel methodology for improved early warning systems in mountainous regions. This research provides valuable insights for disaster prevention and could be adapted to other geohazard-prone areas worldwide.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101719"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial representativeness of NO2 monitoring stations with respect to Sentinel-5P satellite based estimates","authors":"Patryk Tadeusz Grzybowski ,&nbsp;Jan Paweł Musiał ,&nbsp;Krzysztof Mirosław Markowicz","doi":"10.1016/j.rsase.2025.101682","DOIUrl":"10.1016/j.rsase.2025.101682","url":null,"abstract":"<div><div>Nitrogen dioxide (NO₂) pollution is one of the most significant environmental threats to human health. To mitigate the negative effects of NO₂ and other air pollutants, it is essential to monitor pollution through a wide and reliable network. This study aimed to demonstrate the feasibility of using estimated NO₂ concentrations derived from Sentinel-5P, which is a mission that is part of the European Earth Observation Programme Copernicus.satellite data, combined with meteorological factors, to support NO₂ pollution monitoring. Unlike point ground measurements, this approach provides data for the entire area of interest. The main objective of this work is to determine what fraction of Poland is covered by spatially representative (SR) surface NO₂ concentrations measured at ground-based stations. Additionally, the study investigated how many people live in areas not covered by SR NO₂ measurements and identified potential locations for new stations to improve the spatial representativeness of the NO₂ monitoring network across Poland. Four methods for determining SR were tested: Global Moran's I, variability of the correlation coefficient with distance from the station, variability of semivariance with distance from the station, and similarity threshold. It was revealed that approximately 74–94 % of the urban population and 10–30 % of the rural population, where the yearly NO₂ limit was exceeded (&gt;10 μg/m³), are covered by the representative NO₂ measurement network, depending on the method used. Finally, it was proposed to add 10–17 new urban stations and 0–5 new rural stations. This would ensure that 91–98 % of the population is covered by the SR monitoring network.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101682"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation of pansharpening algorithms on Worldview-4 satellite imagery over Western Himalaya","authors":"Dhiraj Kumar Singh ,&nbsp;George P. Petropoulos ,&nbsp;Dileep Kumar Gupta ,&nbsp;Sartajvir Singh ,&nbsp;Vishakha Sood ,&nbsp;Spyridon E. Detsikas","doi":"10.1016/j.rsase.2025.101677","DOIUrl":"10.1016/j.rsase.2025.101677","url":null,"abstract":"<div><div>This study compares component substitution (CS) and multiresolution analysis (MRA) pansharpening algorithms applied to high-resolution WorldView-4 imagery over the Indian Western Himalaya. The performance of these methods was evaluated using quantitative (i.e., visual assessment) and qualitative metrics (such as Relative Average Spectral Error (RASE), Root Mean Square Error (RMSE), Error Relative Global Dimensionless Synthesis (ERGAS), Bias, and the Fidelity-Deformation (FD) metric). The FD metric captures both spectral fidelity and spatial structure preservation by integrating localized and global error measures. The results indicated that MRA-based approaches (i.e., ATWT_M2, M3, and MTF_GLP) exhibit reduced spectral distortions, as reflected by lower Bias and RASE values, making them suitable for applications that demand high spectral fidelity. In contrast, CS-based approaches, such as HCS and BDSD, achieved lower ERGAS and RMSE values, suggesting improved spatial detail preservation. Overall, although pansharpened imagery may be advantageous for developing fine-resolution applications, the choice of the pansharpening algorithm should be made carefully, considering the specific application.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"39 ","pages":"Article 101677"},"PeriodicalIF":4.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}